Autism Spectrum Disorder (ASD) is widely recognized for many decades, yet there are no definitive or universally accepted diagnostic criteria. [1]. Most diagnostic systems and measures consider ASD to be a 3-symptom cluster disorder with varying severity and etiology. This is reflected in the diagnostic systems of Diagnostic and Statistical Manual of Mental Disorders- IV Text Revision (DSM-IV TR) and International Classification of Diseases-10 (ICD-10), as well as the various measures that evolved from them [2,3]. However, the field trials of the draft version of Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) have supported a 2-symptom cluster model [4,5] of varying severity.

The construct, core and behavioral symptoms as well as the reliability of the diagnosis of ASD in diverse socio-cultural settings using the available tools have been problematic. In addition to the evolving construct of ASD globally, the timing of the presentation of this group of disorders [6] and racial differences have been documented to bring about variations in the core symptoms and associated behavioral features [7]. In multi-centric studies, the diagnostic distinctions among sub-categories of ASD has been unreliable across centers even while using standard diagnostic instruments, supporting a shift from a categorical to dimensional approach in the diagnosis of ASD [8]. The use of detailed and explicit appropriateness criteria has significantly enhanced the diagnostic yield in other medical disciplines [9]. Furthermore, currently available diagnostic instruments for ASD are patented [10,11], not available in different Indian languages, and fee is to be paid each time the instrument is used. To overcome several of these limitations, INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD) was developed for identification and diagnosis of ASD using appropriateness criteria developed for Indian context.

Development of Appropriateness Criteria and Instrument

A team of 49 national experts from different parts of India and six international experts (pediatricians, child psychiatrists, pediatric neurologists, epidemiologist, pediatric otorhinolanringologists, clinical psychologists, special educators, specialist nurses, speech therapist, occupational therapists and social scientists) developed the appropriateness criteria and tool over three rounds of 2-day workshop in 2006-2007. During this process, the clinical criteria for ASD as presented in the ICD-10, DSM-IV TR Autism Diagnostic Observation Schedule (ADOS), Childhood Autism Rating Scale (CARS), Gilliam Autism Rating Scale (GARS), Modified Checklist for Autism in Toddlers (M-CHAT) and clinicians’ views on the construct of ASD were reviewed. Pools of items were selected by the panel using the modified Delphi technique [12]. From the pool of items, the symptoms were rank-ordered by the panel members, and further reduced using endorsement rate approach [13].

The instrument was first developed in English, and forward and backward translated to Hindi and Malayalam by two teams with two independent, bilingual translators in each, to achieve the proximity of the source and target versions. This instrument was piloted (first pilot psychometric evaluation round, described below). Based on the feedback, the instrument was modified in Hindi version and then forward translated to English, Malayalam and six additional Indian languages (Odia, Konkani, Urdu, Khasi, Gujarati and Telugu) and backward translated in a similar manner.

The first round of psychometric (pilot) testing for INDT-ASD was done with 266 children at two sites (New Delhi-178 and Thiruvananthapuram-88). These included 81 children of ASD, 120 with other neuro-developmental disorders (NDDs) and 65 with typical development. However, as the ability of INDT-ASD to differentiate autism from other NDDs (specificity 69%) and the agreement with CARS [14] was moderate (kappa=0.69), further training and modifications in translation (12 items), item changes (3 items) and reframing (7 items with new examples) was done.

In the second round, psychometric (field) testing of the INDT-ASD was conducted in four public sector tertiary-care pediatric referral centers: All India Institute of Medical Sciences (AIIMS), Maulana Azad Medical College (MAMC), and Lady Hardinge Medical College (LHMC), New Delhi; and Child Development Centre (CDC) in Thiruvananthapuram.

Consecutive children (2-9 yr) with written informed consent from their primary caregivers were enrolled into the study from the Child development and Child neurology outpatient clinics. The children were recruited until the a priori sample size was reached. The validation exercise was conducted from June 2008 to April 2010.

Fig. 1 Process of subject grouping, randomization and assessment for psychometric validation of INDT-ASD.

Fig. 1 depicts the method for patient selection, assessment and interview. At every center, the study coordinator, who was not part of any assessment, evaluated the children attending the clinic for eligibility and enrolled them in the study. The subjects were randomly allocated into Group A (N=78) and Group B (N=76). In Group A, INDT-ASD was administered followed by CARS and the sequence was reversed in Group B. This was done by independent psychologists to minimize rating bias. Thereafter, each child was assessed by a two-member expert team (pediatric neurologist and child psychiatrist) who based their diagnosis on DSM-IV TR criteria. The diagnostic protocol required approximately four hours over two days, and consisted of (i) a face-to-face interview with parent/primary caregiver, as well as (ii) direct observations of children in play activities. Each evaluator was blinded to original diagnosis and assessment results of other evaluator; their evaluations were separately sealed in opaque envelops immediately after the assessment.

Sample size and sampling technique: Sample size for diagnostic accuracy was calculated assuming the sensitivity and specificity of INDT-ASD to diagnose ASD to be 85%, with a precision of ±10% at 95% confidence level. The sample size was calculated to be 50 children in each of the three categories: ASD, other NDD, and normal development. This sample was also adequate for exploratory factor analysis during validation [15]. CARS was used to study the convergent validity of INDT-ASD as well as divide the participants in to mild and severe autism groups [16]. The Stanford-Binet Intelligence Scale (SK Kulshreshtha, Hindi version) (SBIS) [17] and Vineland Social Maturity Scale (VSMS) [18] were used in all subjects to measure divergent validity of INDT-ASD.

A comprehensive and structured three day training workshop was conducted for psychologists using standardized manual developed for INDT-ASD, CARS, SBIS, and VSMS. Separate training groups (A and B; Fig. 1) of psychologists for INDT-ASD and CARS were created. Both groups were trained to administer SBIS and VSMS. Two pediatric neurologists and two child psychiatrists with over ten years of professional experience were the trainers.

Data processing and statistical analysis: Participants’ assessment details were entered into a pre-designed instrument with unique identification numbers. Statistical analysis was done using SPSS (version 19) and MedCalc (version 12.2.1.0) after data was entered into Intelligent Character Recognition (ICR) sheets. These were processed using ABBYY Form Reader 4.0 software. Psychometric parameters of diagnostic accuracy, construct validity, criterion validity and internal consistency of INDT-ASD were estimated. The performance of INDT-ASD was compared with CARS for convergent validity.

IndiaCLEN Review Board and the local Institutional Review Boards/Ethical Committees of all the participating centers provided approval for the study. Written informed consent was obtained from parent/primary caregiver and verbal assent from children, whenever possible. At the end of the assessment, parents were informed regarding the child’s needs and appropriate referrals were facilitated, when warranted.

The mean (SD) age of enrolled children was 64.2 (25.3) months (n=154; 110 boys). Ninety children had average and 64 had subnormal intelligence.

According to the expert diagnosis based on DSM-IV TR (considered as the gold standard), 51 children were diagnosed as ASD: autism (44), Pervasive developmental disorder not otherwise specified (PDD-NOS) (5), Rett syndrome (1), and CDD (1). Severe ASD was present in 41 and mild-to-moderate in 10 children. Forty-nine "Other NDDs" included intellectual disability (29), neuro-motor impairments including cerebral palsy (6), epilepsy (4), attention-deficit/hyperactivity disorder (2), vision/hearing impairment (2), and speech and language disorder (1); multiple NDDs were noted among 5 children.

Diagnosis of ASD involves eliciting extensive history, and detailed observation by experienced clinicians and psychologists. The sensitivity and specificity of ASD of INDT-ASD was 98% and 95.1%, respectively in our study. This is better than the performance documented in the DSM-III field trial [19] and the ICD-10 field trial [20]. The DSM-IV TR [21] sensitivity of 93% is closer to our findings, although the specificity was only 78%. The specificity of INDT-ASD is similar to that of DSM-5 (95%) but the sensitivity is much higher (76%) [22].

High Cronbach’s alpha coefficient for internal consistency demonstrated that the symptom clusters of INDT-ASD as used in the Indian context were homogeneous. Some previous studies for homogeneity of the symptom groups support our findings [21], whereas others differ with the present findings [23,24].

The agreement rate between INDT-ASD and DSM-IV-TR (used by expert team) is better than that reported for the DSM-III, DSM-III-R and ICD-10 [19]. Our false positive rate of 5.8% is comparable with that of the values reported for DSM-III but lower than that of DSM-III-R [19]. In our study, children with cerebral palsy and intellectual disability had impairment in proto-declarative and proto-imperative pointing and were poorly communicative. This possibly resulted in false positivity of INDT-ASD. The false negative diagnosis was also far lower than that reported for DSM-III and DSM-III-R [19]. The only false negative case in the current study had low-average Intelligence quotient (IQ) with mild autism symptoms.

The convergence between the INDT-ASD and CARS was high suggesting that the construct of autism as measured by INDT-ASD and CARS are theoretically related to each other. The negative correlation between INDT-ASD and IQ as measured by SBIS shows that INDT-ASD has the ability to diverge from theoretical constructs that are different from its own, like construct of autism from other childhood disabilities. However, data for comparison of the concurrent validity for DSM and ICD are not readily available in the literature.

Establishing the appropriateness of the international criteria in the Indian context enhances the possibility of accurate clinical diagnosis and paves way to developing as well as validating new measures for autism in India. With similar approach and appropriate modification, tools may be developed for use in other resource limited settings.

Contributors: All authors have contributed, designed and approved the study. NKA will act as a guarantor for this work.

Funding: Ministry of Social Justice and Empowerment (National Trust), National Institute of Health (NIH-USA); Fogarty International Center (FIH), and Autism Speaks (USA).

Competing interests: None stated.

1. Volkmar F, State M, Klin A. Autism and autism spectrum disorders: diagnostic issues for the coming decade. J Psychol Psychiatry. 2009; 50:108-15.

2. The International Classification of Disease (ICD-10): Classification of Mental and Behavioral Disorders. Clinical Descriptions and Diagnostic Guidelines: World Health Organization, Geneva; 2002.

3. American Psychiatric Association (APA). Diagnostic and Statistical Manual of Mental Disorders (4th ed, text rev.) (DSM-IV-TR): Washington, DC; 2000.

4. Mandy WP,　Charman T,　Skuse DH. Testing the construct validity of proposed criteria for DSM-5 autism spectrum disorder. J Am Acad Child Adolesc Psychiatry.　2012; 51:41-50.

5. Frazier TW,　Youngstrom EA,　Speer L,　Embacher R,　Law P,　Constantino J, et al. Validation of proposed DSM-5 criteria for autism spectrum disorder. J Am Acad Child Adolesc Psychiatry.　2012; 51:28-40.

6. Bakare MO, Munir KM. Excess of non-verbal cases of autism spectrum disorders presenting to orthodox clinical practice in Africa - a trend possibly resulting from late diagnosis and intervention. S Afr J Psychiatr. 2011; 17:118-20.

7. Sell NK,　Giarelli E,　Blum N,　Hanlon AL,　Levy SE. A comparison of autism spectrum disorder DSM-IV criteria and associated features among African American and white children in Philadelphia County. Disabil Health J. 2012; 5:9-17.

8. Lord C, Petkova E, Hus V, Gan W, Lu F, Martin DM, et al. A multisite study of the clinical diagnosis of different autism spectrum disorders. Arch Gen Psychiatry. 2012; 69:306-13.

9. de Bosset V, Froehlich F, Rey JP, Thorens J, Schneider C, Wietlisbach V, et al. Do explicit appropriateness criteria enhance the diagnostic yield of colonoscopy? Endoscopy. 2002; 34:360-8.

10. Lord C, Risi S, Lambrecht L, Cook EH Jr, Leventhal BL, DiLavore PC, et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000; 30:205-23.

11. Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24:659-85.

12. Fischer RG. The Delphi method: a description, review and criticism. J Acad Librarianship. 1978:64-70.

13. Fitzpatrick R, Davey C, Buxton MJ, Jones DR. Evaluating patient-based outcome measures for use in clinical trials. Health Technol Assess. 1998; 2:1-74.

14. Schopler E, Reichler RJ, DeVellis RF, Daly K. Toward objective classification of childhood autism: Childhood Autism Rating Scale (CARS).　J Autism Dev Disord.　1980; 10:91-103.　

15. McColl E, Jacoby A, Thomas L, Soutter J, Bamford C, Steen N, et al. Design and use of questionnaires: a review of best practice applicable to surveys of health service staff and patients. Health Technol Assess. 2001; 5:1-256.

16. Russell　AJ, Mataix-Cols　D, Anson　MAW, Murphy DGM. Psychological treatment for obsessive-compulsive disorder in people with autism spectrum disorders – A pilot study. Psychother Psychosom. 2009; 78:59-61.

17. Kulsrestha SK. Stanford Binet Intelligence Scale and Manual.　Hindi Adaptation (Under third revision) Allahabad: Manas Seva Sansthan Prakashan; 1971.

18. Doll EA. A generic scale of social maturity. Am J Orthopsychiatry. 1935; 5:180-8.

19. Volkmar FR, Cicchetti DV, Dykens E, Sparrow SS, Leckman JF, Cohen DF. An evaluation of the Autism Behavior Checklist. J Autism Dev Disord. 1988; 18:81-97.

20. Spitzer RL, Siegel B. The DSM-III-R field trial of pervasive developmental disorders. J Am Acad Child Adoles Psychiatry. 1990; 29:855-62.

21. Volkmar FR, Klin A, Siegal B, Szatmari P, Lord C, Campbell M, et al. Field trial for autistic disorder in DSM-IV. Am J Psychiatry. 1994; 151:1361-7.

22. McPartland JC, Reichow B, Volkmar FR. Sensitivity and specificity of proposed DSM-5 diagnostic criteria for autism spectrum disorder.　J Am Acad Child Adolesc Psychiatry. 2012; 51:368-83.

23. Ronald A, Happe F, Bolton P, Butcher LM, Price TS, Wheelwright S, et al. Genetic heterogeneity between the three components of the autism spectrum: A twin study. J Am Acad Child Adolesc Psychiatry. 2006; 45:691–9.

24. Ronald A, Happe F, Price TS, Baron-Cohen S, Plomin R. Phenotypic and genetic overlap between autistic traits at the extremes of the general population. J Am Acad Child Adolesc Psychiatry. 2006; 45:1206–14.

25. Tanguay PE, Robertson J, Derrick A. A dimensional classiûcation of autism spectrum disorder by social communication domains. J Am Acad Child Adolesc Psychiatry. 1998; 37:271-7.

26. Tadevosyan-Leyfer O,　Dowd M,　Mankoski R,　Winklosky B,　Putnam S,　McGrath L, et al. A principal component analysis of the Autism Diagnostic Interview-Revised. J Am Acad Child Adolesc Psychiatry. 2003; 42:864-72.

27. Charman T,　Taylor E,　Drew A,　Cockerill H,　Brown JA,　Baird G. Outcome at 7 years of children diagnosed with autism at age 2: predictive validity of assessments conducted at 2 and 3 years of age and pattern of symptom change over time.　Child Psychol Psychiatry. 2005; 46:500-13.　

28. Sigman M, McGovern CW. Improvement in cognitive and language skills from preschool to adolescence in autism. J　Autism　Dev Disord. 2005; 35:15-23.　

29. Van Lang NDJ, Boomsma A, Sytema S, de Bildt AA, Kraijer DW, Ketelaars C, et al. Structural equation analysis of a hypothesized symptom model in the autism spectrum. J Child Psychol Psychiatry. 2006; 47:37-44.